kenya poverty data Uganda poverty data Tanzania poverty data Tanzania poverty data

library(plotly)
library(tidyverse)
library(data.table)
library(sf)
library(DT)
library(tmap)
library(ggthemes)
poverty_ken <- fread("poverty_data/poverty_ken.csv")

poverty_uga <- fread("poverty_data/poverty_uga.csv")

poverty_tza <- fread("poverty_data/poverty_tza.csv")

poverty_rwa <- fread("poverty_data/poverty_rwa.csv")

poverty_ea <- rbind(poverty_ken, poverty_uga, poverty_rwa, poverty_tza)

poverty_ea[sample(nrow(poverty_ea), 10)] %>% datatable(options = list(scrollX = TRUE))
nms_old <- names(poverty_ea) 
nms_old
## [1] "Country Name"   "Country ISO3"   "Year"           "Indicator Name"
## [5] "Indicator Code" "Value"
nms_new <- nms_old %>% tolower() 
nms_new <- gsub("\\s", "_", nms_new)
nms_new
## [1] "country_name"   "country_iso3"   "year"           "indicator_name"
## [5] "indicator_code" "value"
setnames(poverty_ea, nms_old, nms_new)
poverty_ea[, value:= as.numeric(value)]
poverty_ea <-  poverty_ea[!is.na(value)]

poverty_ea[sample(nrow(poverty_ea), 10)] %>% datatable(options = list(scrollX = TRUE))
poverty_ea[, year := as.numeric(year)]

poverty_ea <- poverty_ea[!grepl("^Annu", indicator_name)]
poverty_ea_split <- split(poverty_ea, f = poverty_ea$indicator_name)
i = 1
n <- length(poverty_ea_split)
my_plots <-htmltools::tagList()
for (i in 1:n) {
    df = poverty_ea_split[[i]]
    my_title = df[, unique(indicator_name)]
    mn = df[, min(year)]
    mx = df[, max(year)]
    breaks = seq(mn, mx,by = 2)
    p = ggplot(df, aes(year, value, group = country_name, color = country_name) ) +
        geom_line()+
        theme_fivethirtyeight()+
        labs(title = my_title, x = "year", y = "%")+
      scale_color_viridis_d(name="")+
      scale_x_continuous(breaks = breaks)
    
    my_plots[[i]] = ggplotly(p)
    
}

my_plots